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Secure Storage of Data on Devices-Android based 7

Secure Storage of Data on Devices-Android based

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Saeed, Sameera Abubaker Mohamed, Marghny Hassan Farouk Mohamed, Mamdouh Hadhramout University Computer Science Department Computers and Information Technology Faculty Mukalla Yemen Assiut University Computer Science Department Assuit Egypt

ISBN: (纸本)9781665414074

Security in today's world is one of the most important considerations when one wants to send, receive and store files containing private information or files simply too large for an email attachment. People are becoming more and more dependent on their mobile phones for performing the mentioned critical functionalities. Therefore, it is very important to protect sensitive information when the mobile is lost or stolen. There are many algorithms and methods used to accomplish data security in mobile devices. In general, cryptography and steganography are two common methods used to secure communications. Recently, the field of biology has been combined with the field of cryptography to produce a new field called deoxyribonucleic acid (DNA) cryptography which is one of the most powerful tools to solve security *** paper proposes a DNA cryptography technique for securing data stored offline in the Android device where users are not aware of the confidentiality of their private data. It is very difficult to predict the one-time pad key that is used as randomly generated and just for one-time. The proposed algorithm uses DNA mapping for dealing with the data as a DNA sequence. Two approaches have been proposed for achieving desired outcomes. © 2021 IEEE.

关键词： DNA

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Towards An Optimized Dragonfly Algorithm Using Hill Climbing Local Search to Tackle the Low Exploitation Problem 7

Towards An Optimized Dragonfly Algorithm Using Hill Climbing...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Emambocus, Bibi Aamirah Shafaa Jasser, Muhammed Basheer Sunway University School of Engineering and Technology Malaysia

ISBN: (纸本)9781665414074

The Dragonfly Algorithm (DA), a swarm intelligence algorithm inspired by the behavior of dragonflies, has been recently proposed and it was found to have a higher performance as compared to other swarm intelligence algorithms. However, it still has certain limitations, and its performance can be further improved. DA has a low exploitation phase, and this leads to problems such as low accuracy of solutions, falling in local optima and low convergence rate. We propose to enhance the exploitation of DA by using the hill climbing algorithm as a local search so as to increase its effectiveness and efficiency in producing high accuracy solutions. The hill climbing algorithm is chosen to be used as a local search for DA since it always optimizes the current solution until the local optima is obtained However, it has not been employed in any existing hybrid of DA. The proposed algorithm performance is to be evaluated by employing it as a training algorithm for an Artificial Neural Network (ANN) to optimize its connection weights. A classification dataset will be used for the training and testing of the ANN trained by the proposed algorithm. The root mean squared error of the ANN will be taken as the objective function of the optimized dragonfly algorithm. The accuracy of the resultant neural network will be compared to that of an ANN trained by the original DA and the time taken for the training process by both algorithms will be compared. Based on the analysis, the optimized algorithm is expected to outperform the original DA by allowing the resultant neural network to have a higher classification accuracy. © 2021 IEEE.

关键词： Neural networks

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Data pre-processing of website browsing record: An initial step for web page classification 7

Data pre-processing of website browsing record: An initial s...

引用

7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Apandi, Siti Hawa Sallim, Jamaludin Mohamed, Rozlina Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pahang Pekan Malaysia

ISBN: (纸本)9781665414074

The Internet utilization has resulted in an increase in the number of web pages on the World Wide Web. The classification of web pages is required to organize the growing number of web pages. A web page classification system is proposed to be constructed using a deep learning algorithm. The initial step for web page classification is data pre-processing. The website browsing record is used as a dataset in this study. The raw dataset needs to be pre-processing to fetch the cleaned data by removing missing value data, redundant data, and error data. There are many steps in data pre-processing which include data cleaning and web content pre-processing. The main contribution of this paper is to investigate how to do data pre-processing on website browsing records that focusing on the Game and Online Video web pages that will be utilized as the dataset to construct the web page classification model. After doing the data pre-processing, the number of datasets will be reduced. This shows many datasets have been removed because it is inactive and not suitable to be used in this study as the dataset of Game and Online Video web pages. © 2021 IEEE.

关键词： Websites

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Pixel-based Feature for Android Malware Family Classification using Machine Learning Algorithms 7

Pixel-based Feature for Android Malware Family Classificatio...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Osman, Mohd Zamri Abidin, Ahmad Firdaus Zainal Romli, Rahiwan Nazar Darmawan, Mohd Faaizie Faculty of Computing College of Computing and Applied Science Universiti Malaysia Pahang Malaysia Faculty of Computer Mathematical Sciences Universiti Teknologi Mara Malaysia

ISBN: (纸本)9781665414074

'Malicious software' or malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. In the past, many solutions have proved ineffective and have resulted in many false positives. Having the ability to identify and classify malware will help prevent them from spreading and evolving. In this paper, we study the effectiveness of the proposed classification of the malware family using a pixel level as features. This study has implemented well-known machine learning and deep learning classifiers such as K-Nearest Neighbours (k-NN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree, and Random Forest. A binary file of 25 malware families is converted into a fixed grayscale image. The grayscale images were then extracted transforming the size 100x100 into a single format into 100000 columns. During this phase, none of the columns are removed as to remain the patterns in each malware family. The experimental results show that our approach achieved 92% accuracy in Random Forest, 88% in SVM, 81% in Decision Tree, 80% in k-NN and 56% in Naïve Bayes classifier. Overall, the pixel-based feature also reveals a promising technique for identifying the family of malware with great accuracy, especially using the Random Forest classifier. © 2021 IEEE.

关键词： Android malware

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Comparison on Machine Learning Algorithm to fast detection of Malicious Web pages 7

Comparison on Machine Learning Algorithm to fast detection o...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Nurulsafawati Wan Manan, Wan Nizam Mohmad Kahar, Mohd Mohd Ali, Noorlin Universiti Malaysia Pahang Faculty of Computing Pahang Malaysia

ISBN: (纸本)9781665414074

The advance growth of technology today, has simply brought to the increasing of the Internet and online activities such as in business, management, education and other areas. However, this has exposed to the malicious activities and online threat to the users which will disrupt the method of every system and performance. Accurate and fast detection on such threats in a timely manner is vital. Furthermore, the scale of the web pages (i.e huge data) and with heterogeneous nature of the web itself has complicate the detection process, and unable to detect accurately. Defining features in detecting malicious web pages is a vital condition in order to generate accurate detection result. These may lead to misdetection of malicious web as it only focus on certain criteria of feature selection. Furthermore, previous approached have used blacklist technique which a conventional method and have shown promising result in detecting malicious webpages. Therefore, implementing the principle of the machine learning, which is training the classification algorithm will be perform to improve the detection accuracy. Output will be evaluated using correctly classified instances and incorrectly classified instances. The WEKA (Waikato Environment for Knowledge Analysis) will be used for testing and generating the comparison output. Selected dataset from well-known resources will be used based on identified features in order to verify the web pages form legitimates ones. Compared to several decision tree method, Random Forest has shown promising and higher sensitivity result towards malicious data which is 98.3% compared to other classification algorithm © 2021 IEEE.

关键词： Websites

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Usability Evaluation of the MeMo Tutor: A Scaffolding-Based Pedagogical Agent to Facilitate Learning 7

Usability Evaluation of the MeMo Tutor: A Scaffolding-Based ...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Dian Martha, Ati Suci Budi Santoso, Harry Junus, Kasiyah Suhartanto, Heru Faculty of Computer Science Universitas Indonesia School of Computing Telkom University Jakarta Indonesia Faculty of Computer Science Universitas Indonesia Depok Indonesia

ISBN: (纸本)9781665414074

Nowadays, distance learning faces challenges in increasing students' cognitive engagement and motivation. Research on pedagogical agents is one of the solutions offered to overcome the challenges. MeMo Tutor, the pedagogical agent in this study, acts as a scaffold using integrated metacognitive and motivational scaffoldings. This study was conducted to evaluate the usability of the MeMo Tutor by measuring the level of user satisfaction when using the application. Measuring usability helps determine how usable (e.g., effective, efficient, easy to learn) the system from users' perspective, determine whether any issues need to be fixed, and monitor the performance of the system design over time. The usability evaluation process involved thirteen participants in the first iteration and fifty-six participants in the second iteration. Evaluation data were collected and analyzed using an online prototype analytical tool and System Usability Scale (SUS). In the first iteration, the usability score shows that users were often confused by ambiguous words, especially if the words were close together. This study shows that the average usability score of the online prototype analytical tool correlates well with the average SUS score in the second iteration. The usability results obtained were 88 fromthe online prototype analytical tool and 81.16 from SUS. The score indicates that users were satisfied with the MeMo Tutor application. We recommend focusing on developing mobile-based or responsive web interfaces to improve user experience. In addition, it is crucial to analyze the relationship between the learner's experience and the learning achievement obtained by the user after using the MeMo Tutor. © 2021 IEEE.

关键词： Motivation

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A Data-oriented Approach for Detecting offensive Language in Arabic Tweets 7

A Data-oriented Approach for Detecting offensive Language in...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Refaee, Eshrag A. Jazan University College of Computer Sciences and Information Technology Jazan Saudi Arabia

ISBN: (纸本)9781665414074

The growing popularity of social media (SM) platforms has made these platforms a crucial part of modern societies. Users from different cultures, backgrounds, demographics get aboard in an increasing manner to express their views, stances, and opinions on a varied range of topics. Since users on SM can easily hide their real identity, a closer look at daily posts on social medial platforms shows that users do not seem to reflect only their stances and views, but also, they get an opportunity for revealing their behaviors, which could be negative towards the others. Although only a small population of SM users can show negative behavior towards other individuals, groups, and society in general, the impact could be catastrophic. This has resulted in the emerge of terms like cyberbullying, online extremism/hatred/threatening, online trolling, online political-polarity discourse. To ensure safe social networking, the domain of automatic detection of offensive/hatred language has lately grown notably. This work focuses on utilizing a publicly available dataset of Arabic tweets labeled for offensive/non-offensive language. Unlike previous work which focuses merely on developing and tuning machine learning models to be as accurate as possible on the benchmark dataset used, we turn to focus on the characteristics of the offensive language used in SM. The purpose is to have an in-depth look into the dataset to disclose what seems to be hidden patterns in offensive language expressed daily online. Our findings reveal the benefit of using larger training dataset that covers a wide range of offensive language patterns to build robust machine learning classifiers with a better ability to generalize well on highly sparse data used in SM. © 2021 IEEE.

关键词： Classification (of information)

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An Organ Donation management System (ODMS) based on Blockchain Technology for Tracking and Security Purposes 7

An Organ Donation Management System (ODMS) based on Blockcha...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Yahaya, Che Akmal Che Firdaus, Ahmad Khen, Yong Yew Yaakub, Che Yahaya Razak, Mohd Faizal Abd University Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan Pahang Darul Makmur26600 Malaysia

ISBN: (纸本)9781665414074

Blockchain is a viable technology for assisting in the deployment of electronic medical records, particularly in terms of data integration and data security. Recent scholarly studies on the use of blockchain for medical record data management have demonstrated that using this technology provides patients with complete control of their medical records. Data must be consistent and correct throughout the data transmission process to be efficient and clear. Decentralized data storage is the most appropriate strategy for storing data to expand storage functionality. The organ management system currently employs a client-server architecture. Because this type of technology stores data on a server, the data is lost and difficult to restore in the event of a server failure. This study proposes a blockchain-based organ donation management system (ODMS). © 2021 IEEE.

关键词： Blockchain

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Midrange Exploration Exploitation Searching Particle Swarm Optimization in Dynamic Environment 7

Midrange Exploration Exploitation Searching Particle Swarm O...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Fauzi, Nurul Izzatie Husna Musa, Zalili Zulkifli, Nor Saradatul Akmar Universiti Malaysia Pahang Faculty of Computing Pahang Pekan26600 Malaysia

ISBN: (纸本)9781665414074

Conventional Particle Swarm Optimization was introduced as an optimization technique for real problems such as scheduling, tracking, and traveling salesman. However, conventional Particle Swarm Optimization still has limitations in finding the optimal solution in a dynamic environment. Therefore, we proposed a new enhancement method of conventional Particle Swarm Optimization called Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO). The main objective of this improvement is to enhance the searching ability of poor particles in finding the best solution in dynamic problems. In MEESPSO, we still applied the basic process in conventional Particle Swarm Optimization such as initialization of particle location, population evolution, and updating particle location. However, we added some enhancement processes in MEESPSO such as updating the location of new poor particles based on the average value of the particle minimum fitness and maximum fitness. To see the performance of the proposed method, we compare the proposed method with three existing methods such as Conventional Particle Swarm Optimization, Differential Evaluation Particle Swarm Optimization, and Global Best Local Neighborhood Particle Swarm Optimization. Based on the experimental result of 50 datasets show that MEESPSO can find the quality solution in term of number of particle and iteration, consistency, convergence, optimum value, and error rate. © 2021 IEEE.

关键词： Location

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Model Evaluation of Various Supervised Machine Learning Algorithm for Heart Disease Prediction 7

Model Evaluation of Various Supervised Machine Learning Algo...

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7th International Conference on Software engineering and Computer systems and 4th International Conference on Computational Science and information management, ICSECS-ICOCSIM 2021

作者： Hameetha Begum, S. Nisha Rani, S.N. Muscat College Muscat Oman Fatima Michael Engineering College Madurai India

ISBN: (纸本)9781665414074

Machine Learning (ML) is a type of Artificial Intelligence (Al) that allows the machine to learn from data and become more accurate in predicting outcomes without human intervention. The fundamental idea of machine learning is to mimic the way our brain works. In a variety of application domains, including medicine and healthcare, ML techniques have demonstrated strong prediction abilities. This study evaluated and compared several popular supervised machine learning techniques for predicting heart diseases using medical records from the UCI Machine learning repository. The performance of different models, including Support Vector Machines (SVM), K-Nearest Neighbor (KNN), and Logistic Regression models, is examined in this paper. The performance of these algorithms was analyzed based on the (Area Under ROC Curve) AUC score. AUC is an evaluation metric that helps us validate our machine learning algorithm how good it is, and the decision is made to implement if the AUC score is more significant than 0.5. The trial result verifies that the Logistic regression algorithm has achieved the highest AUC score of 0.87 compared to other ML algorithms. © 2021 IEEE.

关键词： Support vector machines

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